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Correlation Analysis of Soil Nutrients and Prediction Model Through ISO Cluster Unsupervised Classification with Multispectral Data

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Abstract

The agricultural sector is the backbone of the Indian economy, where precision agriculture is playing a vital role in boosting productivity. The soil chemical parameters play an important role in precision agriculture. Analysis and prediction of micronutrient of the soil chemical parameters are highly insisted upon by farmers and agriculture researchers. The soil nutrients parameters can be  analyzed from multispectral data through ISO cluster unsupervised classification. Understanding the nutrient level of soil is highly recommended  for farming; early understanding may help to improve soil fertility and fully meet productivity requirements. This paper addresses the soil nutrients analysis by the regression method and its spectral indexed based on the prediction model by Iterative Self-Organizing (ISO) cluster unsupervised classification algorithm. Therefore,  for this study, the Baggi, Ibrahimpur, Wai, Mogra, and Bori (for validation) villages were selected. These villages are located in the Amravati district and fall under Maharashtra provenance of India. From the study area, three soil nutrients parameters such as P, Fe, pH indices-based data were acquired from the spectral calculation of Sentinel-2 and Landsat-8 bands. The P, Fe and pH indices were calculated from the yellowness index (YI), ferrous oxide index, and carbonate level. The ISO cluster unsupervised mechanism has been used for prediction of soil chemical parameter indices out of 100 samples. The study regions recognition rate is 97% for P, 94.05% for Fe, and 69% for pH. During the validation process, four villages' results were used for the identification of soil nutrient parameters for Bori village. The results of the study area can be helpful for the development of the planning of soil fertility in the agriculture fields and effectively increase the crop yield production in the semi-arid region.

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Abbreviations

ISO:

Iterative Self-Organizing

P:

Phosphorus

Fe:

Ferric Oxide

pH:

Potential of Hydrogen

RS:

Remote Sensing

GIS:

Geographic Information System

GPS:

Global Positioning System

EC:

Electrical Conductivity

OC:

Organic Carbon Content

MLP:

Machine Learning Programming

NDVI:

Normalized Difference Vegetation Index

YI:

Yellowness Index

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Authors and Affiliations

  1. Department of Electronics and Telecommunication, Sipna College of Engineering and Technology, Amravati, Maharashtra, India

    Viraj A. Gulhane

  2. Department of Electronics and Telecommunication, Sipna College of Engineering and Technology, Amravati, Maharashtra, India

    Sandeep V. Rode

  3. Center for Climate Smart Agriculture and Water Management, University, MPKV, Rahuri, Maharashtra, India

    Chaitanya B. Pande

Authors
  1. Viraj A. Gulhane
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  2. Sandeep V. Rode
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  3. Chaitanya B. Pande
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Contributions

1. MLP processing of satellite and soil data.

2. Developed an algorithm based on four villages' soil and satellite data.

3. Validated four villages soil data and correlation  with Bori village prdication results.

4.  Developed model and algorithm; that can be helpfulful to pricesion farming and study of soil nutrients.

Corresponding author

Correspondence to Viraj A. Gulhane.

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Gulhane, V.A., Rode, S.V. & Pande, C.B. Correlation Analysis of Soil Nutrients and Prediction Model Through ISO Cluster Unsupervised Classification with Multispectral Data. Multimed Tools Appl 82, 2165–2184 (2023). https://doi.org/10.1007/s11042-022-13276-2

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  • DOI: https://doi.org/10.1007/s11042-022-13276-2

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